AbstractThis paper
proposes a fully automated 3D reconstruction and visualization system
for architectural scenes (interiors and exteriors). The reconstruction
of indoor environments from photographs is particularly challenging due
to texture-poor planar surfaces such as uniformly-painted walls. Our
system first uses structure-from-motion, multi-view stereo, and a
stereo algorithm specifically designed for Manhattan-world scenes
(scenes consisting predominantly of piece-wise planar surfaces with
dominant directions) to calibrate the cameras and to recover initial 3D
geometry in the form of oriented points and depth maps. Next, the
initial geometry is fused into a 3D model with a novel depth-map
integration algorithm that, again, makes use of Manhattan-world
assumptions and produces simplified 3D models. Finally, the system
enables the exploration of reconstructed environments with an
interactive, image-based 3D viewer. We demonstrate results on
several challenging datasets, including a 3D reconstruction and
image-based walk-through of an entire floor of a house,
the first result of this kind from an automated computer vision system.

Acknowledgements
This work was supported in part by National Science Foundation grant
IIS-0811878, SPAWAR, the Office of Naval Research, the University of
Washington Animation Research Labs, and Microsoft. We thank Christian
Laforte and Feeling Software for the kitchen dataset. We
also thank Eric Carson and Henry Art Gallery for the help with
the gallery
dataset.